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[2602.04634] WideSeek-R1: Exploring Width Scaling for Broad Information Seeking via Multi-Agent Reinforcement Learning
Summary
The paper introduces WideSeek-R1, a multi-agent reinforcement learning framework aimed at improving broad information seeking by enhancing organizational capability through width scaling.
Why It Matters
As tasks in AI become broader, the ability to efficiently organize and execute multiple agents is crucial. This research addresses the limitations of existing systems by proposing a novel framework that optimizes the collaboration of agents, potentially leading to significant advancements in AI applications requiring extensive information retrieval.
Key Takeaways
- WideSeek-R1 utilizes a lead-agent-subagent framework for better task orchestration.
- The framework is trained using multi-agent reinforcement learning to enhance parallel execution.
- Experimental results show significant performance improvements with increased subagent numbers.
- The approach addresses the shift from depth to width scaling in AI tasks.
- WideSeek-R1's performance is comparable to single-agent systems, indicating its effectiveness.
Computer Science > Artificial Intelligence arXiv:2602.04634 (cs) This paper has been withdrawn by Zelai Xu [Submitted on 4 Feb 2026 (v1), last revised 13 Feb 2026 (this version, v2)] Title:WideSeek-R1: Exploring Width Scaling for Broad Information Seeking via Multi-Agent Reinforcement Learning Authors:Zelai Xu, Zhexuan Xu, Ruize Zhang, Chunyang Zhu, Shi Yu, Weilin Liu, Quanlu Zhang, Wenbo Ding, Chao Yu, Yu Wang View a PDF of the paper titled WideSeek-R1: Exploring Width Scaling for Broad Information Seeking via Multi-Agent Reinforcement Learning, by Zelai Xu and 9 other authors No PDF available, click to view other formats Abstract:Recent advancements in Large Language Models (LLMs) have largely focused on depth scaling, where a single agent solves long-horizon problems with multi-turn reasoning and tool use. However, as tasks grow broader, the key bottleneck shifts from individual competence to organizational capability. In this work, we explore a complementary dimension of width scaling with multi-agent systems to address broad information seeking. Existing multi-agent systems often rely on hand-crafted workflows and turn-taking interactions that fail to parallelize work effectively. To bridge this gap, we propose WideSeek-R1, a lead-agent-subagent framework trained via multi-agent reinforcement learning (MARL) to synergize scalable orchestration and parallel execution. By utilizing a shared LLM with isolated contexts and specialized tools, WideSeek-R1 jointly optimizes ...